Amazon RDS for Aurora vs Liquibase: What are the differences?
Amazon RDS for Aurora: MySQL and PostgreSQL compatible relational database with several times better performance. Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability; Liquibase: Source control for your database. Developers store database changes in text-based files on their local development machines and apply them to their local databases. Changelog files can be be arbitrarily nested for better management.
Amazon RDS for Aurora and Liquibase are primarily classified as "SQL Database as a Service" and "Database" tools respectively.
Some of the features offered by Amazon RDS for Aurora are:
- High Throughput with Low Jitter
- Push-button Compute Scaling
- Storage Auto-scaling
On the other hand, Liquibase provides the following key features:
- Supports code branching and merging
- Supports multiple developers
- Supports multiple database types
"MySQL compatibility " is the top reason why over 11 developers like Amazon RDS for Aurora, while over 12 developers mention "Great database tool" as the leading cause for choosing Liquibase.
Liquibase is an open source tool with 1.78K GitHub stars and 1.09K GitHub forks. Here's a link to Liquibase's open source repository on GitHub.
Medium, StackShare, and Zumba are some of the popular companies that use Amazon RDS for Aurora, whereas Liquibase is used by Viadeo, Orbitz, and Virgin Pulse. Amazon RDS for Aurora has a broader approval, being mentioned in 121 company stacks & 31 developers stacks; compared to Liquibase, which is listed in 15 company stacks and 12 developer stacks.
What is Amazon RDS for Aurora?
What is Liquibase?
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Over the years we have added a wide variety of different storages to our stack including PostgreSQL (some hosted by Heroku, some by Amazon RDS) for storing relational data, Amazon DynamoDB to store non-relational data like recommendations & user connections, or Redis to hold pre-aggregated data to speed up API endpoints.
Since we started running Postgres ourselves on RDS instead of only using the managed offerings of Heroku, we've gained additional flexibility in scaling our application while reducing costs at the same time.
We are also heavily testing Amazon RDS for Aurora in its Postgres-compatible version and will also give the new release of Aurora Serverless a try!
#SqlDatabaseAsAService #NosqlDatabaseAsAService #Databases #PlatformAsAService
Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.
I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.
For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.
Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.
Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.
Future improvements / technology decisions included:
Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic
As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.
One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.
Flyway vs Liquibase #Migration #Backwards-compatible
We were looking for a tool to help us integrating the migration scripts as part of our Deployment. At first sight both tools look very alike, are well integrated with Spring, have a fairly frequent development activity and short release cycles.
Liquibase puts a lot of emphasis on independence with the DB, allowing you to create the scripts on formats like JSON and YML, abstracting away from SQL, which it's also supported. Since we only work with one DB type across services we wouldn't take much advantage of this feature.
Flyway on the other hand has the advantage on being actively working on the integration with PostgreSQL 11, for it's upcoming version 6. Provides a more extensive set of properties that allow us to define what's allowed on what's not on each different environment.
Instead of looking for a tool that will allow us to rollback our DB changes automatically, we decided to implement backwards-compatible DB changes, for example adding a new column instead of renaming an existing one, postponing the deletion of the deprecated column until the release has been successfully installed.
Managed MySQL clustered database so I dont have to deal with the required infrastructure
Core database for managing users, teams, tests, and result summaries
We moved our database from compose.io to AWS for speed and price.